Influence of open-source virtual-reality based gaze training on navigation performance in Retinitis pigmentosa patients in a crossover randomized controlled trial

Methods A group of RP patients (n = 8, aged 20-60) participated in a study consisting of two 4-week-phases, both carried out by the same patient group in randomized order: In the ‘training phase’, participants carried out a Virtual-Reality gaze training for 30 minutes per day; In the ‘control phase’, no training occurred. Before and after each phase, participants were tasked to move through a randomized real-world obstacle course. Navigation performance in the obstacle course as well as eye-tracking data during the trials were evaluated. The study is registered at the German Clinical Trials Register (DRKS) with the ID DRKS00032628. Results On average, the time required to move through the obstacle course decreased by 17.0% after the training phase, the number of collisions decreased by 50.0%. Both effects are significantly higher than those found in the control phase (p < 0.001 for required time, p = 0.0165 for number of collisions), with the required time decreasing by 5.9% and number of collisions decreasing by 10.4% after the control phase. The average visual area observed by participants increases by 4.41% after training, however the effect is not found to be significantly higher than in the control phase (p = 0.394). Conclusion The performance increase over the training phase significantly surpasses the natural learning effect found in the control phase, suggesting that Virtual-Reality based gaze training can have a positive effect on real-world navigation tasks for patients with RP. The training is available as work-in-progress open-source software.

As was described in the 'Suggested gaze pattern' section, the patients' gaze movements during training were measured, and the similarity between this gaze movement and the suggested gaze pattern was calculated at run-time.To do so, the first step is to analyze the eye-tracking data captured by the VR device to determine saccades, as described in the section 'Saccade characteristics and gaze pattern similarity'.Using a modified Multimatch-Algorithm [32] , sections of multiple saccades executed by the participant are compared to a saccade representation of the suggested gaze pattern (Fig. 12) to calculate a similarity value based on how well the two saccade patterns match.This similarity value was displayed to participants after each trial of the Gaze Training, giving them a quantitative measure of how closely their gaze movements match the suggested gaze pattern.1°horizontal and vertical angle, resulting a two-dimensional array consisting of 360 * 180 individual sections.Next, the eye tacking data, which was captured both within the VR training as well as in real-world trials, is analyzed.For the calculation of the DVF, three parameters are extracted from each eye tracking sample: The time stamp at which the sample was captured, the elevation angle and azimuth angle of the gaze direction measured at that specific time.In addition to these parameters, the static VF size of the respective participant, as reported in table 1, is required for the calculation of the DVF.
For each eye tracking sample, the current gaze direction is projected onto a point (x, y) within the two-dimensional array.If the participant's gaze is directed forward, the gaze direction would be mapped exactly in the center of the array at position (180, 90).If the gaze then shifts, for example, by 10°to the right or left, the projected position on the array would change to (190,90) or (170, 90), respectively.If the gaze shifts upwards or downwards by 10°, the resulting projected position in the array would be (180, 100) or (180, 80).The next step is to project not just the gaze direction to the grid, but the entire VF of the participant.In other words, it must be determined which sections in the array are currently covered by the participant's VF.To achieve this, the following formula is utilized: Here, x grid and y grid describe the horizontal and vertical position of the section in the grid, x gaze and y gaze describe the projected position of the gaze in the grid, and r V F is the average VF radius of the participant.Each section that is identified to be within the participants VF is annotated with the time stamp of the current eye tracking sample.If the section already contains a time stamp, the old time stamp is overwritten with the newer one.This results in each section of the array containing information about the last time stamp at which it was covered by the VF -or, in other words, the last time it was observed by the participant.The subsequent step to determine the DVF involves iterating through each individual section in the two-dimensional array, counting the number n observed of sections annotated with a time stamp that falls within the specified time interval.For example, with a specified interval of three seconds, n observed would include all sections with time stamps less than three seconds old.To enhance the interpretability of the DVF output, it is reported as a percentage of the visual area that could be observed by a static healthy VF with approximated dimensions of 180 * 135.In summary, the DVF is calculated as DV F = n observed 180 * 135 * 100%.This calculation is performed for every eye tracking sample measured within a trial.The average of all calculated values yields the DVF for the respective trial, reported in the supplementary file S1.
Saccade detection Fig. 14 visualizes the saccade detection approach described in the section 'Real-world obstacle course measurements' in the point 'Saccade characteristics and gaze pattern similarity'.Appendix D -Statistical models and QQ-plots This section lists full details on the models used for the statistical analysis of the real-world obstacle course results, as well as the QQ-plots used to visualize normal distribution of results.

Figure 12 :
Figure 12: Visualization of a Multimatch-based comparison between a gaze pattern displayed by a participant (Real Gaze Pattern) and an ideal representation of the suggested gaze pattern.Each square displays the difference in angle and amplitude between the respective saccade vectors, with 0.0 meaning saccades are identical and 1.0 meaning saccades are complete opposites.Colorized squares indicate the "path of the least resistance" determined by the Multimatchalgorithm, which describes the best match between the two patterns.

Figure 14 :
Figure 14: Visualization of the saccade detection following the algorithm of Nyström et al. [40].The displayed data is taken from one of the real-world obstacle course trials, measured with the Pupil Labs Invisible eye tracker [36].

Figure 15 :
Figure 15: Results of the three parameters saccade frequency, ratio of exploratory saccades, and ratio between vertical and horizontal saccades.Exploratory saccade ratio shows the number of exploratory saccades divided by the total number of saccades per trial.Vertical to horizontal saccade ratio is calculated as the average y-components of a saccade divided by the average x-component.A ratio of 1 would indicate an equal amount of vertical and horizontal eye movements.
Figure 16: QQ-plots of the residuals of different result parameters of the real-world obstacle course.